5,441 research outputs found

    SIMDAT

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    Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation

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    A human computation system can be viewed as a distributed system in which the processors are humans, called workers. Such systems harness the cognitive power of a group of workers connected to the Internet to execute relatively simple tasks, whose solutions, once grouped, solve a problem that systems equipped with only machines could not solve satisfactorily. Examples of such systems are Amazon Mechanical Turk and the Zooniverse platform. A human computation application comprises a group of tasks, each of them can be performed by one worker. Tasks might have dependencies among each other. In this study, we propose a theoretical framework to analyze such type of application from a distributed systems point of view. Our framework is established on three dimensions that represent different perspectives in which human computation applications can be approached: quality-of-service requirements, design and management strategies, and human aspects. By using this framework, we review human computation in the perspective of programmers seeking to improve the design of human computation applications and managers seeking to increase the effectiveness of human computation infrastructures in running such applications. In doing so, besides integrating and organizing what has been done in this direction, we also put into perspective the fact that the human aspects of the workers in such systems introduce new challenges in terms of, for example, task assignment, dependency management, and fault prevention and tolerance. We discuss how they are related to distributed systems and other areas of knowledge.Comment: 3 figures, 1 tabl

    Integrating Data Science and Earth Science

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    This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows

    Towards Collaborative Scientific Workflow Management System

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    The big data explosion phenomenon has impacted several domains, starting from research areas to divergent of business models in recent years. As this intensive amount of data opens up the possibilities of several interesting knowledge discoveries, over the past few years divergent of research domains have undergone the shift of trend towards analyzing those massive amount data. Scientific Workflow Management System (SWfMS) has gained much popularity in recent years in accelerating those data-intensive analyses, visualization, and discoveries of important information. Data-intensive tasks are often significantly time-consuming and complex in nature and hence SWfMSs are designed to efficiently support the specification, modification, execution, failure handling, and monitoring of the tasks in a scientific workflow. As far as the complexity, dimension, and volume of data are concerned, their effective analysis or management often become challenging for an individual and requires collaboration of multiple scientists instead. Hence, the notion of 'Collaborative SWfMS' was coined - which gained significant interest among researchers in recent years as none of the existing SWfMSs directly support real-time collaboration among scientists. In terms of collaborative SWfMSs, consistency management in the face of conflicting concurrent operations of the collaborators is a major challenge for its highly interconnected document structure among the computational modules - where any minor change in a part of the workflow can highly impact the other part of the collaborative workflow for the datalink relation among them. In addition to the consistency management, studies show several other challenges that need to be addressed towards a successful design of collaborative SWfMSs, such as sub-workflow composition and execution by different sub-groups, relationship between scientific workflows and collaboration models, sub-workflow monitoring, seamless integration and access control of the workflow components among collaborators and so on. In this thesis, we propose a locking scheme to facilitate consistency management in collaborative SWfMSs. The proposed method works by locking workflow components at a granular attribute level in addition to supporting locks on a targeted part of the collaborative workflow. We conducted several experiments to analyze the performance of the proposed method in comparison to related existing methods. Our studies show that the proposed method can reduce the average waiting time of a collaborator by up to 36% while increasing the average workflow update rate by up to 15% in comparison to existing descendent modular level locking techniques for collaborative SWfMSs. We also propose a role-based access control technique for the management of collaborative SWfMSs. We leverage the Collaborative Interactive Application Methodology (CIAM) for the investigation of role-based access control in the context of collaborative SWfMSs. We present our proposed method with a use-case of Plant Phenotyping and Genotyping research domain. Recent study shows that the collaborative SWfMSs often different sets of opportunities and challenges. From our investigations on existing research works towards collaborative SWfMSs and findings of our prior two studies, we propose an architecture of collaborative SWfMSs. We propose - SciWorCS - a Collaborative Scientific Workflow Management System as a proof of concept of the proposed architecture; which is the first of its kind to the best of our knowledge. We present several real-world use-cases of scientific workflows using SciWorCS. Finally, we conduct several user studies using SciWorCS comprising different real-world scientific workflows (i.e., from myExperiment) to understand the user behavior and styles of work in the context of collaborative SWfMSs. In addition to evaluating SciWorCS, the user studies reveal several interesting facts which can significantly contribute in the research domain, as none of the existing methods considered such empirical studies, and rather relied only on computer generated simulated studies for evaluation

    ASCR/HEP Exascale Requirements Review Report

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    This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
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